import gradio as gr
from utils import (
    sobel_edge_detection,
    canny_edge_detection,
    hough_lines,
    laplacian_edge_detection,
    contours_detection,
    prewitt_edge_detection,
    gradient_magnitude,
    corner_detection,
)
import cv2
import numpy as np


def predict_image(algorithm, image):
    # Apply edge detection (e.g., Canny)
    edges = cv2.Canny(image, 50, 150, apertureSize=3)

    # Apply Hough Line Transform
    lines = cv2.HoughLines(edges, 1, np.pi / 180, threshold=100)

    # Draw lines on the original image
    for line in lines:
        rho, theta = line[0]
        a = np.cos(theta)
        b = np.sin(theta)
        x0 = a * rho
        y0 = b * rho
        x1 = int(x0 + 1000 * (-b))
        y1 = int(y0 + 1000 * (a))
        x2 = int(x0 - 1000 * (-b))
        y2 = int(y0 - 1000 * (a))
        cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)
    
    return edges

GrImage = gr.Image()
GrDropdown = gr.Dropdown(
    [
        "Sobel Edge Detection",
        "Canny Edge Detection",
        "Hough Lines",
        "Laplacian Edge Detection",
        "Contours Detection",
        "Prewitt Edge Detection",
        "Gradient Magnitude",
        "Corner Detection",
    ]
)

GrOutput = gr.Image()

iface = gr.Interface(fn=predict_image, inputs=[ GrDropdown,GrImage], outputs=GrOutput)
iface.launch()